Systems, methods and devices for neural network control for IPM motor drives

ABSTRACT

Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to provisional patent application Ser.No. 62/805,478 filed on Feb. 14, 2019 and titled “SYSTEMS, METHODS ANDDEVICES FOR NEURAL NETWORK CONTROL FOR IPM MOTOR DRIVES.” The disclosureof which is hereby incorporated by reference in its entirety.

BACKGROUND

There are two main types of the permanent magnet (PM) synchronous motorsused in electric vehicles (EVs): surface-mounted PM (SPM) motors andinterior-mounted PM (IPM) motors. For an SPM motor, the magnets of themotor are on the surface of the rotor. For an IPM motor, the magnets areburied inside the rotor. Due to the requirement of a large operatingspeed range for an electric vehicle, IPM motors are widely used in theEV industry.

Efficiency and torque are important factors for EVs. In terms of PMmotors for EVs, improving efficiency and driving torque requires 1)minimizing energy loss and 2) maximizing voltage utilization. From theenergy loss perspective, at each EV operating condition, an efficientreference command needs to be generated for control of an IPM motorwhich requires accurate motor parameter information. From the voltageutilization standpoint, it is usually required to extend the motoroperation from linear to over or even six-step modulation regions.

Therefore, what is desired are improved control, parameter estimation,and optimization systems for controlling and operating interior-mountedpermanent magnet motors using multiple neural networks, including anANN-based ADP for an IPM, as well as neural network based sensorlessspeed estimation methods.

SUMMARY

Methods, systems and devices are described for improving the performanceof IPM motors in EVs by use of one or more of: 1) a neural network (NN)controller trained according to the approximate dynamic programming(ADP) principle to provide the most efficient and reliable controlsolution for IPM motor operation in linear, over and six-step modulationregions; 2) an NN estimator to identify practical IPM motor parametersat different real-time operating conditions; and 3) an NN mapper toreplace the traditional lookup table mechanism for more accurate mappingfrom stator d- and q-axis currents to motor parameters. Overall, new andintelligent technologies are utilized to greatly enhance EV motordriving torque, efficiency, reliability and adaptivity, which results ina potentially transformational impact to automobile industry.

Described herein is a method and system for controlling aninterior-mounted permanent magnet (IPM) alternating-current (AC)electrical machine utilizing a space vector pulse-width modulated(SVPWM) converter operably connected between an electrical power sourceand the IPM AC electrical machine and a configuration comprising threeneural networks (NNs), including a controller NN operably connected tothe SVPWM converter, a parameter estimator NN, and a flux-weakening andMTPA NN. The controller NN integrates one or more of conventional PI(proportional-integral) control techniques, predictive controltechniques, PR (proportional-resonant) control techniques, optimalcontrol techniques and sliding mode control characteristics togetherinto one controller to provide high performance drives for IPM motoroperating in linear modulation or overmodulation conditions. Theestimator NN estimates motor parameters in real-time and provides ahigh-performance sensor-less control technology so as to reduce motorcost and size, probability of failure, and boost motor reliability. Theflux-weakening and MTPA NN generates optimal d- and q-axis currentreferences based on variable motor parameters obtained from theestimator NN so as to allow the controller NN to drive the IPM motorefficiently.

Embodiments of a method for controlling an interior-mounted permanentmagnet (IPM) alternating-current (AC) electrical machine are disclosedherein. In one embodiment, a space vector pulse-width modulated (SVPWM)converter operably connected between an electrical power source and theIPM AC electrical machine is provided. A neural network vector controlsystem is operably connected to the SVPWM converter, the neural networkvector control system comprises a controller neural network configuredand trained to implement an approximate dynamic programming (ADP)algorithm, wherein the neural network vector control system is trainedat times when the IPM AC electrical machine is not operating. Aparameter estimator neural network is provided. The parameter estimatorneural network receives as inputs actual d and q-axis currents andactual d and q-axis voltages representative of current and voltageoutput from the SVPWM converter, wherein the parameter estimator neuralnetwork captures and estimates parameters for the IPM AC electricalmachine. An adjustable IPM motor model is applied with the parameterestimator neural network to provide estimated d and q-axis currents,wherein the estimated d and q-axis currents are compared with the actuald and q-axis currents and are fed back as inputs to the estimator neuralnetwork, and the parameter estimator neural network is trained toimplement an ADP algorithm and the parameter estimator neural networksystem is trained at times when the IPM AC electrical machine is notoperating. A flux weakening and maximum torque per ampere (MTPA) neuralnetwork is provided. The flux-weakening and MTPA neural network receivesas an input a desired torque command and receives as inputs from theparameter estimator neural network L_(d), L_(q), ω_(e), and Ψ_(pm),wherein L_(d) and L_(q) are stator d- and q-axis inductances, ω_(e) isthe IPM AC electrical machine's electrical rotational speed, and Ψ_(pm)is a flux linkage of a permanent rotor magnet of the IPM AC electricalmachine, and wherein the rotor position of the IPM AC electrical machineis estimated from ω_(e) using the parameter estimator neural network andthe rotor position is used to convert the current and voltage outputsfrom the SVPWM converter to their d and q-axis currents and d and q-axisvoltages representative of current and voltage output from the SVPWMconverter.

In some embodiments, the neural network vector control system includescontroller neural network, wherein the controller neural network is afeedforward network comprising an input layer, multiple hidden layersand an output layer, wherein the controller neural network: receives aplurality of inputs at the input layer, wherein the plurality of inputsinclude at least reference d and q-axis stator currents from theflux-weakening and MTPA neural network and receives d and q-axis actualstator currents as well as other signals that can be generated from thereference and actual d and q-axis stator currents, such as error signalsbetween the reference and actual stator currents and integrals of theerror signals; outputs a compensating dq-control voltage at the outputlayer, wherein the controller neural network is configured to optimizethe compensating dq-control voltage based on the plurality of inputs;and controls the SVPWM converter using the compensating dq-controlvoltage. The controller neural network is trained as a recurrent networkto minimize a cost function of the ADP algorithm using a forwardaccumulation through time (“FATT”) algorithm. The cost function can beused to measure how close the actual motor d and q-axis stator currentsmatch the reference d and q-axis stator currents and the controllerneural network is trained offline at times when the IPM AC electricalmachine is not operating.

In some embodiments the parameter estimator neural network is afeedforward network comprising an input layer, multiple hidden layersand an output layer, wherein said parameter estimator neural network:receives a plurality of inputs at the input layer, wherein the pluralityof inputs include at least d and q-axis actual stator currents andestimated d and q-axis stator currents from an adjustable IPM motormodel as well as other signals that can be generated from the actual andestimated d and q-axis stator currents, such as error signals betweenthe estimated and actual stator currents and integrals of the errorsignals; outputs estimated motor parameters L_(d), L_(q), ω_(e), andΨ_(pm) at the output layer, wherein the parameter estimator neuralnetwork together with adjustable IPM motor model is configured tooptimize the IPM motor parameter estimation based on the plurality ofinputs. The parameter estimator neural network is trained as a recurrentnetwork to minimize a cost function of the ADP algorithm using a forwardaccumulation through time (“FATT”) algorithm, wherein the cost functioncan be used to measure how close the estimated d and q-axis statorcurrents from the adjustable IPM motor model match the actual d andq-axis stator currents. The adjustable IPM motor model receivesestimated motor parameters L_(d), L_(q), ω_(e), and Ψ_(pm) from theparameter estimator neural network as well as the actual d and q-axisstator voltages of the IPM motor, and outputs the estimated d and q-axisstator currents. The parameter estimator neural network is trainedoffline at times when the IPM AC electrical machine is not operating,wherein from the estimated motor parameters, an electrical rotationalspeed of the IPM AC electrical machine and a rotor position of the IPMAC electrical machine can be obtained, and wherein the estimatedelectrical rotational speed and the estimated rotor position can enablesensorless control for an IPM AC electrical machine.

In some embodiments, the flux-weakening and MTPA neural network is afeedforward network comprising an input layer, multiple hidden layersand an output layer, wherein the flux-weakening and MTPA neural network:receives a plurality of inputs at the input layer, wherein the pluralityof inputs include at least a reference torque as well as estimated motorparameters L_(d), L_(q), ω_(e), and Ψ_(pm) from the parameter estimatorneural network; and outputs reference d and q-axis stator currents tothe controller neural network at the output layer. The flux-weakeningand MTPA neural network is trained to minimize a cost function using aLevenberg-Marquardt or backpropagation training algorithm, wherein thecost function can be used to measure how close the reference d andq-axis stator currents from the flux-weakening and MTPA neural networkmatch the ideal optimal reference d and q-axis stator currents fordifferent reference torque and motor parameters. The flux-weakening andMTPA neural network is trained offline at times when the IPM ACelectrical machine is not operating.

In various applications, the disclosed IPM AC electric motor can be usedin an electric vehicle, industrial robot, or electric drone.

In some instances, the electrical power source comprises a battery orother dc power sources.

Also disclosed herein are embodiments of a system for controlling aninterior-mounted permanent magnet (IPM) alternating-current (AC)electrical machine. One embodiment of the system comprises a spacevector pulse-width modulated (SVPWM) converter operably connectedbetween an electrical power source and the IPM AC electrical machine; aneural network vector control system operably connected to the SVPWMconverter, the neural network vector control system comprising aprocessor and a controller neural network configured to implement anapproximate dynamic programming (ADP) algorithm for training the neuralnetwork vector control system when the IPM AC electrical machine is notoperating; a parameter estimator neural network operably connected to anoutput of the SVPWM converter through a dq-axis converter, wherein theparameter estimator neural network comprises a processor and wherein theparameter estimator neural network is configured to receive as inputs dand q-axis currents and d and q-axis voltages representative of currentand voltage output from the SVPWM converter, captures and estimatesparameters for the IPM AC electrical machine including at least anelectrical rotational speed of the IPM AC electrical machine and a rotorposition of the IPM AC electrical machine, and implement an ADP trainingalgorithm, wherein an adjustable IPM motor model is applied with theparameter estimator neural network to provide the estimated d and q-axiscurrents, wherein the estimated d and q-axis currents are compared withthe actual d and q-axis currents and fed-back as inputs to the estimatorneural network, wherein the parameter estimator neural network system istrained at times when the IPM AC electrical machine is not operating; aflux weakening and maximum torque per ampere (MTPA) neural networkoperably connected to the neural network vector control system and theparameter estimator neural network, wherein the flux-weakening and MTPAneural network comprises at least a processor and is configured toreceive as an input a desired torque command and receive as inputs fromthe parameter estimator neural network L_(d), L_(q), ω_(e), and Ψ_(pm),wherein L_(d) and L_(q) are stator d- and q-axis inductances, ω_(e) isthe IPM AC electrical machine's electrical rotational speed, and Ψ_(pm)is a flux linkage of a permanent rotor magnet of the IPM AC electricalmachine, and wherein the rotor position of the IPM AC electrical machineis estimated from ω_(e) using the parameter estimator neural network andthe rotor position is used to convert the current and voltage outputsfrom the SVPWM converter to their d and q-axis currents and d and q-axisvoltages representative of current and voltage output from the SVPWMconverter.

In some embodiments, the controller neural network is a feedforwardnetwork comprising an input layer, multiple hidden layers and an outputlayer, wherein the controller neural network: receives a plurality ofinputs at the input layer, wherein the plurality of inputs include atleast reference d and q-axis stator currents from the flux-weakening andMTPA neural network and receives d and q-axis actual stator currents aswell as other signals that can be generated from the reference andactual d and q-axis stator currents, such as error signals between thereference and actual stator currents and integrals of the error signals;outputs a compensating dq-control voltage at the output layer, whereinthe controller neural network is configured to optimize the compensatingdq-control voltage based on the plurality of inputs; and controls theSVPWM converter using the compensating dq-control voltage, wherein thecontroller neural network is trained as a recurrent network to minimizea cost function of the ADP algorithm using a forward accumulationthrough time (“FATT”) algorithm, wherein the cost function can be usedto measure how close the actual motor d and q-axis stator currents matchthe reference d and q-axis stator currents, wherein the controllerneural network is trained offline at times when the IPM AC electricalmachine is not operating.

In some embodiments, the parameter estimator neural network is afeedforward network comprising an input layer, multiple hidden layersand an output layer, wherein said parameter estimator neural network:receives a plurality of inputs at the input layer, wherein the pluralityof inputs include at least d and q-axis actual stator currents andestimated d and q-axis stator currents from an adjustable IPM motormodel as well as other signals that can be generated from the actual andestimated d and q-axis stator currents, such as error signals betweenthe estimated and actual stator currents and integrals of the errorsignals; and outputs estimated motor parameters L_(d), L_(q), ω_(e), andΨ_(pm) at the output layer, wherein the parameter estimator neuralnetwork together with adjustable IPM motor model is configured tooptimize the IPM motor parameter estimation based on the plurality ofinputs; wherein the parameter estimator neural network is trained as arecurrent network to minimize a cost function of the ADP algorithm usinga forward accumulation through time (“FATT”) algorithm, wherein the costfunction can be used to measure how close the estimated d and q-axisstator currents from the adjustable IPM motor model match the actual dand q-axis stator currents, wherein the adjustable IPM motor modelreceives estimated motor parameters L_(d), L_(q), ω_(e), and Ψ_(pm) fromthe parameter estimator neural network as well as the actual d andq-axis stator voltages of the IPM motor, and outputs the estimated d andq-axis stator currents, wherein the parameter estimator neural networkis trained offline at times when the IPM AC electrical machine is notoperating, wherein from the estimated motor parameters, an electricalrotational speed of the IPM AC electrical machine and a rotor positionof the IPM AC electrical machine can be obtained, wherein the estimatedelectrical rotational speed and the estimated rotor position can enablesensorless control for an IPM AC electrical machine.

In some embodiments, the flux-weakening and MTPA neural network is afeedforward network comprising an input layer, multiple hidden layersand an output layer, wherein the flux-weakening and MTPA neural network:receives a plurality of inputs at the input layer, wherein the pluralityof inputs include at least a reference torque as well as estimated motorparameters L_(d), L_(q), ω_(e), and Ψ_(pm) from the parameter estimatorneural network; and outputs reference d and q-axis stator currents tothe controller neural network at the output layer; wherein theflux-weakening and MTPA neural network is trained to minimize a costfunction using a Levenberg-Marquardt or backpropagation trainingalgorithm, wherein the cost function can be used to measure how closethe reference d and q-axis stator currents from the flux-weakening andMTPA neural network match the ideal optimal reference d and q-axisstator currents for different reference torque and motor parameters,wherein the flux-weakening and MTPA neural network is trained offline attimes when the IPM AC electrical machine is not operating.

In some instances, the disclosed systems can be used where an IPM ACelectric motor is used in an electric vehicle, industrial robot, orelectric drone. In some instances, the electrical power source of thesystem comprises a battery or other dc power sources.

Other systems, methods, features and/or advantages will be or may becomeapparent to one with skill in the art upon examination of the followingdrawings and detailed description. It is intended that all suchadditional systems, methods, features and/or advantages be includedwithin this description and be protected by any accompanying claims inthis application or any application that claims priority to thisapplication.

BRIEF DESCRIPTION OF THE DRAWINGS

The components in the drawings are not necessarily to scale relative toeach other. Like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 shows a simple schematic of a conventional IPM motor drive andcontrol structure;

FIG. 2 illustrates an embodiment of a neural network (NN)-based IPMmotor drive and control structure;

FIG. 3 illustrates an embodiment of an artificial NN (ANN)-basedcurrent-loop control structure;

FIG. 4 illustrates an example of an estimator NN for IPM motor parameterestimation;

FIG. 5 illustrates an exemplary current vector trajectory for maximumtorque per ampere (MTPA) and flux weakening;

FIG. 6 illustrates an exemplary feedback loop between the NN and otherparts of the combined NN and motor feedback system over time;

FIG. 7 illustrates an exemplary method for controlling an IPM ACelectrical machine;

FIG. 8 illustrates an exemplary method for operating a controller NN inan IPM AC machine;

FIG. 9 illustrates an exemplary method for operating an estimator NN inan IPM AC machine; and

FIG. 10 illustrates an exemplary computer comprising at least aprocessor and a memory that can be used for controlling a permanentmagnet motor.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art. Methods and materials similar or equivalent to those describedherein can be used in the practice or testing of the present disclosure.As used in the specification, and in the appended claims, the singularforms “a,” “an,” “the” include plural referents unless the contextclearly dictates otherwise. The term “comprising”, and variationsthereof as used herein is used synonymously with the term “including”and variations thereof and are open, non-limiting terms. The terms“optional” or “optionally” used herein mean that the subsequentlydescribed feature, event or circumstance may or may not occur, and thatthe description includes instances where said feature, event orcircumstance occurs and instances where it does not.

Before the present methods and systems are disclosed and described, itis to be understood that the methods and systems are not limited tospecific synthetic methods, specific components, or to particularcompositions. It is also to be understood that the terminology usedherein is for the purpose of describing particular embodiments only andis not intended to be limiting.

As used in the specification and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. Ranges may be expressed herein as from “about” oneparticular value, and/or to “about” another particular value. When sucha range is expressed, another embodiment includes from the oneparticular value and/or to the other particular value. Similarly, whenvalues are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms anotherembodiment. It will be further understood that the endpoints of each ofthe ranges are significant both in relation to the other endpoint, andindependently of the other endpoint.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where said event or circumstance occurs and instances where itdoes not.

Throughout the description and claims of this specification, the word“comprise” and variations of the word, such as “comprising” and“comprises,” means “including but not limited to,” and is not intendedto exclude, for example, other additives, components, integers or steps.“Exemplary” means “an example of” and is not intended to convey anindication of a preferred or ideal embodiment. “Such as” is not used ina restrictive sense, but for explanatory purposes.

Disclosed are components that can be used to perform the disclosedmethods and systems. These and other components are disclosed herein,and it is understood that when combinations, subsets, interactions,groups, etc. of these components are disclosed that while specificreference of each various individual and collective combinations andpermutation of these may not be explicitly disclosed, each isspecifically contemplated and described herein, for all methods andsystems. This applies to all aspects of this application including, butnot limited to, steps in disclosed methods. Thus, if there are a varietyof additional steps that can be performed it is understood that each ofthese additional steps can be performed with any specific embodiment orcombination of embodiments of the disclosed methods.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices.

Embodiments of the methods and systems are described below withreference to block diagrams and flowchart illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general-purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the examples included therein and to the Figures and their previousand following description.

Stringent emission laws & regulations and increased demand forfuel-efficient, high-performance vehicles are compelling the automobilemanufacturers worldwide to equip their vehicles with alternative fueltechnologies, resulting in increased overall market adoption of electricvehicles (EVs). The global EV market was valued at $103,342 million in2016, and is projected to reach $350,963 million by 2023, growing at aCAGR of 19.8% from 2017 to 2023. The key EV manufacturers such as ToyotaMotor Corporation, Volkswagen AG, and Tesla, expanded their business byopening various research and manufacturing facilities globally. Inaddition, they have launched more efficient and advanced EVs to enhancetheir market share.

However, due to the limited space within an EV, high performance andefficiency of EV electric and electronic components is critical for thegrowth of the EV market. One of the most important EV components iselectric motors. EVs use one or more electric motors or traction motorsfor propulsion. The most widely used electric motor in the automobileindustry are permanent magnet synchronous motors (PMSM). There are twomain types of permanent magnet (PM) motors: SPM motors and IPM motors.Due to the requirement for operation over a wide speed range, IPMmotors, suitable to operate at both low and high speeds, are the primarymotor used by the EV industry. Significant R&D investment has been madeby the automobile companies to improve IPM motor performance, efficiencyand reliability to meet EV operation requirements over a variety ofconditions.

To improve performance, efficiency and reliability of IPM motors, it isdesired to 1) minimize energy loss, 2) maximize voltage utilizationespecially for driving torque demand at high speed, and 3) implementsensorless-based control technology. From an energy loss perspective, ateach EV operating condition, an efficient reference command may begenerated for control of an IPM motor. From a voltage utilizationstandpoint, space vector pulse width modulation (SVPWM) is usually used,instead of the traditional sinusoidal pulse width (SPWM) modulationscheme, for controlling an IPM motor through power inverters. Furtherimprovement of efficiency requires SVPWM to be extended from traditionallinear modulation regions to over modulation regions. From a sensorlesscontrol viewpoint, the automobile industry has researched sensorlesscontrol technologies for years in order to reduce the cost and improvethe reliability for EV driving.

Presently, like other ac electric machines, an IPM is controlled basedon vector control technology. A typical conventional field-orientedvector controller for an IPM motor has two decoupled d- and q-axiscurrent controllers to fulfill the magnetic field and torque control ofthe motor, respectively. In practical EV applications, a referencetorque is first generated according to the driving requirement of an EVunder different road conditions. Then, the torque reference is convertedinto d- and q-axis current references. The torque of an IPM motor isrelated to the stator current of the motor by the equation (1):τ_(em) =p(Ψ_(pm) i _(sq)+(L _(d) −L _(q))i _(sd) i _(sq))  (1)where p is pole pairs, i_(sd) and i_(sq) are the d- and q-axiscomponents of instant stator current, L_(d) and L_(q) are the stator d-and q-axis inductances, and Ψ_(pm) is the flux linkage produced by thepermanent magnet. If the torque computed from (1) is positive, the motoroperates in a drive mode; otherwise, it operates in a regenerating mode.

According to (1), for a given torque command τ*_(em), there are multiplecurrent vectors that can generate the specified torque. The currentvector, which has a minimum magnitude, is called the maximum torque perampere (MTPA) current and represents the operating condition of theminimum copper loss for the commanded torque. However, determination ofthe current references needs to consider two additional constraints: therated current limit Idq_max of the motor and the PWM saturation limit,considering the maximum voltage Vdq_max that can be generated by thepower inverter and applied to the motor. This would result in anoptimization formulation as shown by:Minimize: |i _(s_dq)=√{square root over (i ² _(sd) +i ² _(sq))}Subject to: τ*_(em) =p(Ψ_(pm) i _(sq)+(L _(d) −L _(q))i _(sd) i _(sq)),√{square root over (i ² _(sd) +i ² _(sq))}≤I _(dq_max), √{square rootover (v ² _(sd) +v ² _(sq))}≤V _(dq_max)

To solve the optimization problem efficiently, it is needed to haveaccurate information about motor parameters such as Ld, Lq and Ψpm whichhowever are dependent on the stator current i_(sd) and i_(sq) and canalso change over time as the EV condition changes from new to used.Conventionally, determining an optimal dq current and the control of theIPM motor are built together as shown by FIG. 1 , in which the threefundamental blocks are a flux-weakening and MTPA block 105, alookup-table block 110 and a current-controller block 115. Thelookup-table block 110 provides motor parameters based on measured motorcurrent, the flux-weakening and MTPA block 105 generates optimal d- andq-axis reference currents, based on the motor parameters provided by thelookup-table block 110 and a desired torque demand τ*_(em) that isneeded for driving an EV, and the current-controller block 115 tracksthe reference currents generated by the flux-weakening and MTPA block105 or tracks the reference currents as much as possible in overmodulation modes due to the physical constraint limitations, in terms ofthe rated current and PWM saturation limits, of an IPM motor. In FIG. 1, the motor speed and position are typically measured by using opticalsensors.

However, there are several limitations associated with the existing IPMmotor drive and control technology. First, the existing standardvector-control technology for an IPM motor is developed by using aninaccurate decoupling mechanism, which causes inefficient and unreliableoperation of an IPM motor especially in over and six-step modulationregions. Second, in the present automobile industry, the motorparameters saved in the lookup table 110 are predetermined at themanufacture stage. However, in actual operating conditions, the motorparameters could change from their predetermined values and could alsobe different between any two motors that even belong to the same motortype. In addition, motor parameters saved using the lookup table 110approach are discrete and multidimensional, which reduces the efficiencyand accuracy to obtain actual motor parameters under complicated motoroperating conditions. Third, the flux-weakening and MTPA block 105 shownin FIG. 1 requires solving the optimization problem, which is very timeconsuming and impossible to implement by using a real-time DSP system.Thus, the existing approaches either use simplified and inaccurate“optimization” methods or use a lookup table 110 in order to avoidsolving the optimization problem, which reduces the accuracy of the fluxweakening plus MTPA block 105 and efficiency of IPM motor drives andcontrol. Fourth, the measurement of rotor speed and position usingoptical encoders increases motor cost, size and probability of failure,and reduces motor reliability. As a result, the sensorless controltechnologies have been attracting attention to the automobile industryover the past two decades, and significant research efforts have beenmade.

For such situations, artificial neural networks (NNs), as universalfunction approximators, would be the most suitable to help overcomingthe challenges. Overall, as disclosed herein, a conventional IPM currentcontroller is replaced with a NN controller trained according toapproximate dynamic programming (ADP), the conventional lookup table 110is replaced with an NN estimator to capture real-time IPM motorparameters as well as rotor speed and position, and the flux weakeningplus MTPA block 105 is replaced by an NN that can quickly provide anoptimal flux-weakening and MTPA solution in real-time conditions.

FIG. 2 is an overview illustration of the use of neural networks for IPMmotor drives and control. As shown, the flux-weakening and MTPA block105 has been replaced with a flux-weakening and MTPA neural network 205,the lookup table block 110 has been replaced with an estimator neuralnetwork 210, and the current-controller block 115 has been replaced witha controller neural network 215.

The IPM motor controller uses the controller NN 215 instead of theconventional PI (proportional-integral) control technique. Thecontroller NN 215 is trained offline based on ADP principles. TheADP-based NN control technique has the potential to integrateconventional PI, predictive, PR (proportional-resonant), optimal, andsliding mode control characteristics together into one controller,making it a “super” controller to meet the IPM motor drive needs inchallenging and uncertain EV operating conditions.

Also, the motor parameter estimator uses the estimator NN 210 to captureand estimate practical motor parameters and motor speed and positionwhich may be changing over time and affected by varying motor operatingenvironments. The estimator NN 210 is also trained based on ADP so thatit can provide accurate, predictive motor-parameter estimation based onmotor operation from time sequence points of view. This ADP-NN basedparameter estimation technique has potential to integrate traditionalmotor-parameter estimation methods together to provide the most accurateestimation of motor parameters for flux-weakening and MTPA managementand for sensorless control of an IPM motor.

Further, the flux-weakening and MTPA NN 205 may be an NN approximatorand may be used to replace the flux-weakening and MTPA block 105. Thisis due to the fact that the optimization problem described herein istime consuming and usually requires a minimum time of 10 s seconds andabove to solve, making it unable to meet real-time control demand of themotor controller. Hence, traditional approaches use simplifiedtechniques, such as lookup tables, to avoid solving the optimizationproblem, which certainly affect flux-weakening and MTPA effectiveness. Awell-trained neural network can overcome these challenges and is capableof adapting in real time to changing conditions without the need forretraining.

The equation (2) shows the state-space equation of an IPM motor, inwhich Rs is the resistance of the stator winding; L_(d) and L_(q) arethe stator d- and q-axis inductances; ω_(e) is the motor electricalrotational speed; v_(sd), v_(sq), i_(sd) and i_(sq) are the d and qcomponents of the instant stator voltage and current, Ψ_(pm) is the fluxof the rotor magnet.

$\begin{matrix}{{\frac{d}{dt}\begin{bmatrix}i_{sd} \\i_{sq}\end{bmatrix}} = {{- {\begin{bmatrix}{R_{s}/L_{d}} & {{- \omega_{e}}{L_{q}/L_{d}}} \\{\omega_{e}{L_{d}/L_{q}}} & {R_{s}/L_{q}}\end{bmatrix}\begin{bmatrix}i_{sd} \\i_{sq}\end{bmatrix}}} + \begin{bmatrix}{v_{sd}/L_{d}} \\{v_{sq}/L_{q}}\end{bmatrix} - \begin{bmatrix}0 \\{\omega_{e}{\psi_{pm}/L_{q}}}\end{bmatrix}}} & (2)\end{matrix}$

The design of a conventional vector controller may be based on adecoupled IPM motor model by neglecting the items involving ω inequation. (2), which may be normally treated as the compensation terms.Hence, the corresponding simplified transfer functions, 1/(RS+s·Ld) and1/(RS+s·Lq), may be used to tune the conventional PI controllers for d-and q-axis loops respectively. After the d- and q-axis controllers aretuned, the compensation terms are added back to the output of the PIcontrollers to formulate the final control action. This approximationwould cause a decoupling inaccuracy. In addition, there are only twoparameters to tune for each PI controller which is hard to meet theoptimal control demand especially as practical motor parameters, interms of Ld, Lq and Ψ_(f), could change over a large range. Issues likehigh overshoot current, low reliability under variable motor parameters,and difficulty to control an IPM motor in over-modulation regions havebeen reported by both industry and academic communities. To overcome thechallenges, the ADP-based NN control strategy based on the complete IPMmotor dynamic equation, given by eq. (2), is disclosed and describedherein.

To develop an ADP-based controller NN 215, an ADP cost-to-go function isdefined. In this disclosure, the ADP cost-to-go function for trainingthe controller NN 215 may be the equation (3), in which i_(sd)* andi_(sq)* represent the stator d- and q-axis reference currents,respectively. The training objective may be to minimize the ADPcost-to-go function (3), i.e., to make the actual motor current i_(sd)and i_(sq) close to the reference motor current i_(sd)* and i_(sq)* asmuch as possible. The training algorithm details are presented herein.

$\begin{matrix}{{C\left( {\overset{\rightarrow}{i}}_{x\_{dq}} \right)} = {\sum\limits_{k = 1}^{N}\left( {\left\lbrack {{i_{sd}(k)} - i_{sd}^{*}} \right\rbrack^{2} + \left\lbrack {{i_{sq}(k)} - i_{sq}^{*}} \right\rbrack^{2}} \right)}} & (3)\end{matrix}$

The controller NN 215 comprises a fully connected deep multi-layerperceptron with weight vector w, an input layer, multiple hidden layers,output nodes, and possible shortcut connections between all pairs oflayers. Inputs to the current-loop NN (i.e., the controller NN 215) mayinclude 1) measured d and q-axis currents, 2) two error signals, 3)integrals of the error signals and 4) predictive signals as shown byFIG. 3 . By this way, the advantages of conventional control techniquesare integrated into the controller NN 215 design while avoiding theirshortcomings in order to implement the optimal vector control suitablefor a broad range of real-life IPM motor drive applications. Forexample, the integral terms may incorporate the advantages ofconventional PI control methods, while the predictive terms incorporatethe advantages of predictive control concepts. This interdisciplinary NNdesign approach greatly enhances the performance and ensures thereliability and adaptive capability of the controller NN 215, especiallyunder practical conditions and physical system constraints.

The disclosed methods contain no system identification NNs that aretypically used when developing an ADP-based controller NN 215. Instead,a mathematical model of the IPM motor as shown by the equation (2) isused but will consider the impact of the variable motor parametersduring the training of the controller NN 215. In the proposed project,the stability of IPM motor drives and control is maintained when thecontrol signal generated by the controller NN 215 is beyond the physicalsystem's constraints such as motor operation in over or six-stepmodulation regions. In prior applications of grid-connected inverters,the controller NN 215 can be designed to hold or block the reactivepower control loop while the effectiveness of the active power controlis not affected when the control signals generated by the controller NN215 are beyond the physical PWM saturation limitations. Similarly, thecontroller NN 215 is able to handle control in over and six-stepmodulation regions.

The current-loop NN controller (i.e., the controller NN 215) is capableof a quick response time, such as 1 ms or 0.1 ms, in order to reducecurrent distortion and torque oscillation of the IPM motor and fulfillthe high-performance motor drive requirement. Hence, considering theintegration in terms of cyber-physical systems (CPS), the NN 215 may betrained offline (see below) under a wide range of circumstances. Thisleads to several benefits for the CPS integration, including thecontroller NN 215 showing sufficient adaptive ability not to needretuning every time the motor's hardware parameters change slightly; thecomputational cost at runtime is extremely low, and easy to implement inlow-cost hardware; and, as training is completed offline, it is notpossible for the weights of the controller NN 215 to destabilize atruntime. In addition, to implement the controller NN 215 in real-time, afast digital signal processor (DSP) such as, for example, a TexasInstruments (TI) TMS32010 or TMS32020 (Texas Instruments Inc., DallasTX) for fast matrix multiplications may be used. The DSP multiplies inmicroseconds large matrices with their sizes only limited by theinternal data memory. To significantly reduce the computing time neededfor the controller NN 215, a best rational approximation to tanh(⋅)and/or a rectified linear unit (ReLU) activation function may be used.Overall, these strategies may allow the controller NN 215 to beimplemented in real-time for efficient CPS integration.

Estimating Motor Parameters, Speed and Position Based on ADP and NN

Position sensors may be necessary for conventional high-performancevector control of IPM motors. However, popular position sensors such asoptical encoders and resolvers are usually bulky, mechanicallyvulnerable, and increase the implementation cost of the entire motordrive system. If the accuracy of the position sensor is unreliable, itmay cause severe instability problems in the control loop. The mainpurpose of sensorless control algorithms is to estimate the position andthe speed of the motor without using a position or a speed sensor. Onthe other hand, precise position estimation should be realized based onaccurate motor parameters. Accurate motor parameter estimation is alsoimportant for effective flux weakening and MPTA as explained herein.However, motor parameters vary during the operation due to hightemperature, demagnetization, etc. With such problems, simultaneousmultiparameter identification (rotor position estimation and parameterestimation of motor inductance and permanent magnet flux) is undoubtedto be necessary in high-performance IPM drives and senseless controlsystem.

The traditional NN methods use feedforward networks and thebackpropagation training algorithm. The methods are also based on thepresent error information between the estimated and actual statorcurrents to estimate the motor parameters. Thus, for all the traditionalmethods, the estimation accuracy and/or convergence are issues that havenot been solved effectively, which influences the flux weakening andMPTA efficiency and heretofore made sensorless control unable to beapplied to IPM motors.

Details of the overall estimator NN 210 concept and structure are shownin FIG. 4 , which comprises an Adjustable Motor Model 405 and an NN 407.The outputs of the NN 407 are the estimated motor parameters which willbe applied to the Adjustable Motor Model 405 as shown by the equation(2). The output of the Adjustable Motor Model 405 is the estimated motorcurrent based on the information of the voltage applied to the motor andthe estimated motor parameters. The estimated motor currents,{circumflex over (l)}_(sd) and {circumflex over (l)}_(sq), feedback tothe NN model 407 and are compared with the actual motor stator currents,i_(sd) and i_(sq), for motor parameter estimation at the next time step.The NN 407 must be trained before used for the parameter estimation.However, unlike conventional NN-based parameter estimation methods, anADP-based strategy is used to train the NN 407 in order to achieveaccurate estimation of motor parameters. The ADP cost-to-go function forthe estimator NN 210 is given by the equation (4), and the NN 407 istrained to minimize the ADP cost-to-go function.

$\begin{matrix}{{C\left( {{\overset{\rightarrow}{i}}_{s\_{dq}},{\overset{\hat{\rightarrow}}{i}}_{s\_{dq}}} \right)} = {\sum\limits_{k = 1}^{N}\left( {\left\lbrack {{i_{sd}(k)} - {{\hat{i}}_{sd}(k)}} \right\rbrack^{2} + \left\lbrack {{i_{sq}(k)} - {{\hat{i}}_{sq}(k)}} \right\rbrack^{2}} \right)}} & (4)\end{matrix}$

Therefore, a significant feature of the ADP-NN based estimation (i.e.,the estimator NN 210) is that the motor parameters are estimated basedon information over a long time period or sequence instead of only thepresent error information. The NN 407 comprises a fully connectedmulti-layer perceptron with a weight vector, an input, hidden, outputlayers, and possible shortcut connections between all pairs of layers.Inputs to the NN 407 include 1) the estimated and actual motor currentsignals, 2) two error signals between the estimated and actual motorcurrents, and 3) integrals of the error signals. Information presentedto the NN 407 allows the NN 407 to gain the benefits of conventionalestimation techniques into the estimator NN 210 design in order toimplement the optimal estimation of motor parameters.

After the training, the estimator NN 210 can integrate the advantages ofconventional least square method, extended Kalman filter (EKF), backelectromagnetic force-based methods, model predictive method, adaptiveapproaches, sliding-mode observer, and non-linear observer together, andprovide a powerful estimation solution for IPM motor drives andsensorless control.

To make it possible for real-time implementation of the estimator NN210, the NN 407 may be trained offline based on ADP. Initially, trainingmay be based on the motor model (equation (2)) and the parameters of themodel may be provided by the manufacturer in a lookup table, forexample. If a new EV is put into operation, new training data in termsof motor voltage and current may be measured, collected, and saved intoa data storage system within the EV. As sufficient data is collected andsaved in the data storage system each day or month, the estimator NN 210may be retrained with the purpose of letting the NN 407 be able tocapture the updated motor parameter characteristics that may change overtime after driving the EV over days or years. Generally, the training ofthe estimator NN 210 may be executed offline such as when the EV parksin a garage. Because there is no online training involved, it is mucheasier to achieve the NN-based real-time parameter estimation of an IPMmotor in EV drives. In addition, unlike the motor current, estimation ofmotor speed, inductance and flux may be made at a much larger samplingtime interval, such as 1 ms and above. This is because the motor speedcannot change instantly due to the motor inertia and the inductance androtor flux estimation needed by the flux-weakening and MTPA block 105(or the flux-weakening and MTPA NN 205) can be made at a much largersampling time too.

Flux-Weakening and MTPA Based on NNs

As explained herein, for a given torque command, the optimizationproblem is solved to get the optimal d- and q-axis reference currents inorder for the most efficient operation of an IPM motor. However, theoptimization problem has two additional constraints: one is the ratedcurrent constraint of the IPM motor and the other is the PWM saturationconstraint of the motor inverter. As a result, depending on motorrunning speed, solving the optimization problem involves four differentportions (i.e., the portions OA, AB, BC, and CD) as shown by FIG. 5 .

In the OA portion (MTPA), only the current constraint equation isconsidered, and the solution of the optimization problem can meet thedemanded torque requirement. In the AB portion (flux weakening I), onlythe voltage constraint equation is considered, and the solution of theoptimization problem can also meet the demanded torque requirement. Inthe BC portion, both the current and voltage constraint equations needto be considered but the solution of the optimization problem cannotmeet the demanded torque requirement.

The CD portion (MTPA) occurs at very high speeds for an infinite speeddrive system. In such a condition, current references are deriveddifferently based on tangential intersection between the torque curvesand the shrinking voltage ellipses. As a result, the correspondingalgorithm needs to consider all the fours portions and solving theoptimization problem is time consuming, which cannot be implemented tomeet the real-time control demand in terms of the CPS integration.Therefore, the existing approaches are to solve the optimization problemusing either simplified methods or lookup table approaches, whichhowever are unable to provide correct and efficient current referencesto the current-loop controller of the IPM motor and thus affect themotor driving efficiency.

To overcome the challenge, an NN approximator is used to implement theoptimal flux-weakening and MTPA functions. In order to achieve thisgoal, the optimization problem is solved offline for a large set ofdifferent torque, speed, and motor parameter conditions. This generatesa large amount of reference d- and q-axis reference current datacorresponding to optimal flux-weakening and MTPA at differentconditions. These data are then used to train an NN approximator. The NNapproximator has a deep neural network structure comprised of an inputlayer, hidden layers, and an output layer. The inputs of the NNapproximator include desired torque, d- and q-axis inductances, fluxlinkage produced by the rotor magnet, and motor running speed. Theoutputs of the NN approximator are the reference d- and q-axis currents.

In some instances, the NN approximator is trained offline by using theEKF algorithm, one of the fastest and most effective NN trainingalgorithms. Other training methods may be used. Since there is no onlinetraining involved, it would be much easier to implement the NNapproximator for real-time control and operation of an IPM motor in EVdrives and to integrate the cyber and physical portions of the systemtogether. In addition, the desired torque command for driving an EV isgenerated at a much larger sampling time such as 10 ms or above, whichwould significantly reduce the computation needed for the NNapproximator. This is because an outer-loop controller for EV mechanicaltraction systems usually responses at a much slower speed than an innercurrent-loop controller. Using these strategies, the NN approximator canbe implemented and integrated with the IPM physical system underpractical and real-time conditions.

Training NNs to Implement ADP-Based Optimal Control and ParameterEstimation

Both the controller NN 215 and the estimator NN 210 may be trained toimplement ADP-based control or parameter estimation. Because each NN(FIG. 3 and FIG. 4 , respectively) receives recurrent feedbacks, thecombined system is treated as a recurrent neural network (RNN). Thiscombined “recurrent network” is shown in FIG. 6 , unrolled in time,illustrating how the different system parts interact with each other.The NNs 607 (i.e., the NNs 607A, 607B, 607C, and 607D) are trained usinga new Forward Accumulation Through Time (FATT) algorithm suitable to HPC(high-performance computing) implementation, and an approach tointegrate FATT with Levenberg-Marquardt Backpropagation (LMBP) toaccelerate RNN training based on ADP. As shown, the NN 607A may receiveinitial system states 601 along with reference signals 605 and mayoutput system equations to the neural network 607B. The NN 607B mayreceive the system equations along with reference signals 605 and mayoutput updated system equations to the neural network 607C. The NN 607Dmay receive the updated system equations along with reference signals605 and may output updated system equations (i.e., the final systemstates 603).

ADP employs the principle of Bellman's optimality and is a very usefultool for solving optimization and optimal control problems. The typicalstructure of discrete-time ADP includes a discrete-time state-spacesystem model and a performance index or cost associated with the system.The ADP cost-to-go function may be equation (3) for the currentcontroller of an IPM motor and may be equation (4) for the motorparameter estimation problem. The objective of the training may be tofind an optimal trajectory of control or estimation action from thecontroller NN 215 or the estimator NN 210 that minimizes the costfunction given by the equations (3) or (4).

To benefit the parallel implementation of the RNN training algorithm onan HPC platform, LMBP may be used to train the controller NN 215 and theestimator NN 210. LMBP combines the speed of Newton's method and theguaranteed convergence of steepest descent; thus, LMBP appears to be thefastest NN training algorithm for a moderate number of networkparameters. However, LMBP cannot train RNNs directly. In order to useLMBP for RNN training, the cost function (equations (3) and (4)) may bemodified by rewriting as shown in the equation (5), where U({right arrowover (e_(dq))}(k))=[i_(sd)(k)−i*_(sd)]²+[i_(sq)(k)−i*_(sq)]² for thecurrent control problem and U({right arrow over(e_(dq))}(k))=[i_(sd)(k)−î_(sd)(k)]²+[i_(sq)(k)−î_(sq)]² for theparameter estimation problem. Then, the gradient

$\frac{\partial C}{\partial\overset{\rightarrow}{W}}$can be written in matrix form (6):

$\begin{matrix}{C = {{{\sum\limits_{k = 1}^{N}{U\left( {\overset{\rightarrow}{e_{dq}}(k)} \right)}}\overset{{{define}\mspace{14mu}{V{(k)}}} = \sqrt{C{({\overset{\rightarrow}{e_{dq}}{(k)}})}}}{\rightleftarrows}C} = {\sum\limits_{k = 1}^{N}\left( {V(k)} \right)^{2}}}} & (5) \\{\frac{\partial C}{\partial\overset{\_}{w}} = {\frac{\partial{\sum\limits_{k = 1}^{N}\left\lbrack {V(k)} \right\rbrack^{2}}}{\partial\overset{\_}{w}} = {{\sum\limits_{k = 1}^{N}{2{V(k)}\;\frac{\partial{V(k)}}{\partial\overset{\_}{w}}}} = {2{J\left( \overset{\rightarrow}{w} \right)}^{\tau}\overset{\rightarrow}{V}}}}} & (6)\end{matrix}$where the Jacobian matrix J({right arrow over (w)}) is given by theequation (7), below. Therefore, the weight update can be expressed bythe equation (8):

$\begin{matrix}{{{J\left( \overset{\rightarrow}{w} \right)} = \begin{bmatrix}\frac{\partial{V(1)}}{\partial w_{1}} & \ldots & \frac{\partial{V(1)}}{\partial w_{M}} \\\vdots & \ddots & \vdots \\\frac{\partial{V(N)}}{\partial w_{1}} & \ldots & \frac{\partial{V(N)}}{\partial w_{M}}\end{bmatrix}},{\overset{\rightarrow}{V} = \begin{bmatrix}{V(1)} \\\vdots \\{V(N)}\end{bmatrix}}} & (7) \\{{\Delta\;\overset{\rightarrow}{w}} = {{- \left\lbrack {{{J\left( \overset{\rightarrow}{w} \right)}^{T}{J\left( \overset{\rightarrow}{w} \right)}} + {\mu\; I}} \right\rbrack^{- 1}}{J\left( \overset{\rightarrow}{w} \right)}^{T}\overset{\rightarrow}{V}}} & (8)\end{matrix}$

The matrix computation shown above, however, may be computationallyexpensive on a sequential machine but is suitable for a parallelmachine. In order to calculate the Jacobian matrix J({right arrow over(w)}) efficiently in HPC environment, a Forward Accumulation ThroughTime (FATT) algorithm may be used. The concept of the FATT algorithm isalso illustrated in FIG. 6 . Generally, in order to calculate theJacobian matrix, every trajectory needs to be expanded forward throughtime. The FATT algorithm computes the Jacobian matrix J({right arrowover (w)}), unrolls the system trajectory and calculates both systemstates together at each time step. Thus, only one loop is needed forunrolling the system trajectory and calculating the Jacobian matrixJ({right arrow over (w)}).

FIG. 7 is an illustration of an example method 700 for controlling aninterior-mounted permanent magnet (IPM) alternating-current (AC)electrical machine. The method 700 may be implemented by a manufactureror entity associated with the IPM AC electrical machine.

At 701, a space vector pulse-width modulated (SVPWM) converter isprovided. The SVPWM converter may be operably connected between anelectrical power source and the IPM AC electrical machine.

At 703, a neural network vector control system is provided. The neuralnetwork vector control system may be operably connected to the SVPWMconverter. The neural network vector control system may include acontroller neural network configured and trained to implement anapproximate dynamic programming (ADP) algorithm. The neural networkvector control system may be trained at times when the IPM AC electricalmachine is not operating.

At 705, a parameter estimator neural network is provided. The parameterestimator neural network may receive as inputs actual d and q-axiscurrents and actual d and q-axis voltages representative of current andvoltage output from the SVPWM converter. The parameter estimator neuralnetwork may capture and estimate parameters for the IPM AC electricalmachine. An adjustable IPM motor model may be applied with the parameterestimator neural network to provide estimated d and q-axis currents. Theestimated d and q-axis currents may be compared with the actual d andq-axis currents and fed-back as inputs to the estimator neural network.The parameter estimator neural network may be trained to implement anADP algorithm and the parameter estimator neural network system may betrained at times when the IPM AC electrical machine is not operating.

At 707, a flux weakening and maximum torque per ampere (MTPA) neuralnetwork is provided. The flux-weakening and MTPA neural network mayreceive as an input a desired torque command and may receive as inputsfrom the parameter estimator neural network L_(d), L_(q), ω_(e), andΨ_(pm). L_(d) and L_(q) may be stator d- and q-axis inductances. ω_(e)may be the IPM AC electrical machine's electrical rotational speed, andΨ_(pm) may be a flux linkage of a permanent rotor magnet of the IPM ACelectrical machine. The rotor position of the IPM AC electrical machinemay be estimated from ω_(e) using the parameter estimator neural networkand the rotor position may be used to convert the current and voltageoutputs from the SVPWM converter to their d and q-axis currents and dand q-axis voltages representative of current and voltage output fromthe SVPWM converter.

FIG. 8 is an illustration of an example method 800 for operating acontroller NN 215 in an interior-mounted permanent magnet (IPM)alternating-current (AC) electrical machine. The method 800 may beimplemented by the controller NN 215. The controller NN 215 may be afeedforward network that includes an input layer, multiple hidden layersand an output layer.

At 801, a plurality of inputs is received at the input layer of thecontroller NN 215. The plurality of inputs may include at leastreference d and q-axis stator currents from the flux-weakening and MTPAneural network and d and q-axis actual stator currents as well as othersignals that can be generated from the reference and actual d and q-axisstator currents, such as error signals between the reference and actualstator currents and integrals of the error signals.

At 803, a compensating dq-control voltage is output at the output layerof the controller NN 215. The controller NN 215 may be configured tooptimize the compensating dq-control voltage based on the plurality ofinputs.

At 805, the SVPWM converter is controlled using the compensatingdq-control voltage.

FIG. 9 is an illustration of an example method 900 for operating anestimator NN 210 in an interior-mounted permanent magnet (IPM)alternating-current (AC) electrical machine. The method 900 may beimplemented by the estimator NN 210 (i.e., the parameter estimator NN).The estimator NN 210 may be a feedforward network that includes an inputlayer, multiple hidden layers and an output layer.

At 901, a plurality of inputs is received at the input layer of theestimator NN 210. The plurality of inputs may include at least d andq-axis actual stator currents and estimated d and q-axis stator currentsfrom an adjustable IPM motor model as well as other signals that can begenerated from the actual and estimated d and q-axis stator currents,such as error signals between the estimated and actual stator currentsand integrals of the error signals.

At 903, estimated motor parameters L_(d), L_(q), ω_(e), and Ψ_(pm) areoutput by the output layer of the estimator NN 210. The estimator NN210,together with the adjustable IPM motor model, may be configured tooptimize the IPM motor parameter estimation based on the plurality ofinputs.

Computing Environment

The above system has been described above as comprised of units. Oneskilled in the art will appreciate that this is a functional descriptionand that the respective functions can be performed by software,hardware, or a combination of software and hardware. A unit can besoftware, hardware, or a combination of software and hardware. The unitscan comprise software for controlling an induction motor. In oneexemplary aspect, the units can comprise a control system that comprisesone or more computing devices that comprise a processor 821 asillustrated in FIG. 10 and described below. As used herein, processorrefers to a physical hardware device that executes encoded instructionsfor performing functions on inputs and creating outputs. The computerand/or processor may be used to comprise all or portions of thedescribed neural networks.

FIG. 10 illustrates an exemplary computer that can be used forcontrolling an interior-mounted permanent magnet (IPM)alternating-current (AC) electrical machine such as an IPM inductionmotor. As used herein, “computer” may include a plurality of computers.The computers may include one or more hardware components such as, forexample, a processor 1021, a random-access memory (RAM) module 1022, aread-only memory (ROM) module 1023, a storage 1024, a database 1025, oneor more input/output (I/O) devices 1026, and an interface 1027.Alternatively, and/or additionally, the computer may include one or moresoftware components such as, for example, a computer-readable mediumincluding computer executable instructions for performing a methodassociated with the exemplary embodiments. It is contemplated that oneor more of the hardware components listed above may be implemented usingsoftware. For example, storage 1024 may include a software partitionassociated with one or more other hardware components. It is understoodthat the components listed above are exemplary only and not intended tobe limiting.

Processor 1021 may include one or more processors, each configured toexecute instructions and process data to perform one or more functionsassociated with a computer for controlling an induction motor. Processor1021 may be communicatively coupled to RAM 1022, ROM 1023, storage 1024,database 1025, I/O devices 1026, and interface 1027. Processor 1021 maybe configured to execute sequences of computer program instructions toperform various processes. The computer program instructions may beloaded into RAM 1022 for execution by processor 1021.

RAM 1022 and ROM 1023 may each include one or more devices for storinginformation associated with operation of processor 1021. For example,ROM 1023 may include a memory device configured to access and storeinformation associated with the computer, including information foridentifying, initializing, and monitoring the operation of one or morecomponents and subsystems. RAM 1022 may include a memory device forstoring data associated with one or more operations of processor 1021.For example, ROM 1023 may load instructions into RAM 1022 for executionby processor 1021.

Storage 1024 may include any type of mass storage device configured tostore information that processor 1021 may need to perform processesconsistent with the disclosed embodiments. For example, storage 1024 mayinclude one or more magnetic and/or optical disk devices, such as harddrives, CD-ROMs, DVD-ROMs, or any other type of mass media device.

Database 1025 may include one or more software and/or hardwarecomponents that cooperate to store, organize, sort, filter, and/orarrange data used by the computer and/or processor 1021. For example,database 1025 may store data related to the control of an inductionmotor. The database may also contain data and instructions associatedwith computer-executable instructions for controlling an inductionmotor. It is contemplated that database 1025 may store additional and/ordifferent information than that listed above.

I/O devices 1026 may include one or more components configured tocommunicate information with a user associated with computer. Forexample, I/O devices may include a console with an integrated keyboardand mouse to allow a user to maintain a database of digital images,results of the analysis of the digital images, metrics, and the like.I/O devices 1026 may also include a display including a graphical userinterface (GUI) for outputting information on a monitor. I/O devices1026 may also include peripheral devices such as, for example, aprinter, a user-accessible disk drive (e.g., a USB port, a floppy,CD-ROM, or DVD-ROM drive, etc.) to allow a user to input data stored ona portable media device, a microphone, a speaker system, or any othersuitable type of interface device.

Interface 1027 may include one or more components configured to transmitand receive data via a communication network, such as the Internet, alocal area network, a workstation peer-to-peer network, a direct linknetwork, a wireless network, or any other suitable communicationplatform. For example, interface 1027 may include one or moremodulators, demodulators, multiplexers, demultiplexers, networkcommunication devices, wireless devices, antennas, modems, and any othertype of device configured to enable data communication via acommunication network.

The figures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various implementations of the present invention.In this regard, each block of a flowchart or block diagrams mayrepresent a module, segment, or portion of code, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theimplementation was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious implementations with various modifications as are suited to theparticular use contemplated.

Any combination of one or more computer readable medium(s) may be usedto implement the systems and methods described hereinabove. The computerreadable medium may be a computer readable signal medium or a computerreadable storage medium. A computer readable storage medium may be, forexample, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to implementations ofthe invention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

Various changes and modifications to the disclosed embodiments will beapparent to those skilled in the art. Such changes and modifications,including without limitation those relating to the chemical structures,substituents, derivatives, intermediates, syntheses, compositions,equationtions, or methods of use of the invention, may be made withoutdeparting from the spirit and scope thereof.

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is no way intended thatan order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

Throughout this application, various publications may be referenced,including the references sited below. The disclosures of thesepublications in their entireties are hereby incorporated by referenceinto this application in order to more fully describe the state of theart to which the methods and systems pertain and to illustrateimprovements over the present state of the art in claimed invention.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of the specificembodiments described herein are presented for purposes of illustrationand description. They are not intended to be exhaustive or to limit theembodiments to the precise forms disclosed. It will be apparent to oneof ordinary skill in the art that many modifications and variations arepossible in view of the above teachings.

What is claimed:
 1. A method for controlling an interior-mountedpermanent magnet (IPM) alternating-current (AC) electrical machine by:providing a space vector pulse-width modulated (SVPWM) converteroperably connected between an electrical power source and the IPM ACelectrical machine; providing a neural network vector control systemoperably connected to the SVPWM converter, the neural network vectorcontrol system comprising a controller neural network configured andtrained to implement an approximate dynamic programming (ADP) algorithm,wherein the neural network vector control system is trained at timeswhen the IPM AC electrical machine is not operating; providing aparameter estimator neural network, wherein the parameter estimatorneural network receives as inputs actual d and q-axis currents andactual d and q-axis voltages representative of current and voltageoutput from the SVPWM converter, wherein the parameter estimator neuralnetwork captures and estimates parameters for the IPM AC electricalmachine, wherein an adjustable IPM motor model is applied with theparameter estimator neural network to provide estimated d and q-axiscurrents, wherein the estimated d and q-axis currents are compared withthe actual d and q-axis currents and fed-back as inputs to the estimatorneural network, and wherein the parameter estimator neural network istrained to implement an ADP algorithm and the parameter estimator neuralnetwork system is trained at times when the IPM AC electrical machine isnot operating; providing a flux weakening and maximum torque per ampere(MTPA) neural network comprising an input layer, multiple hidden layersand an output layer, wherein the flux-weakening and MTPA neural networkreceives as an input a desired torque command and receives as inputsfrom the parameter estimator neural network at the input layer areference torque L_(d), L_(q), ω_(e), and ψ_(pm), wherein L_(d) andL_(q) are stator d- and q-axis inductances, ω_(e) is the IPM ACelectrical machine's electrical rotational speed, and ψ_(pm) is a fluxlinkage of a permanent rotor magnet of the IPM AC electrical machine,and wherein the rotor position of the IPM AC electrical machine isestimated from co, using the parameter estimator neural network and therotor position is used to convert the current and voltage outputs fromthe SVPWM converter to their d and q-axis currents and d and q-axisvoltages representative of current and voltage output from the SVPWMconverter, wherein the flux-weakening and MTPA neural network outputsthe d and q-axis currents to the controller neural network at the outputlayer as reference d and q-axis stator currents, wherein theflux-weakening and MTPA neural network is trained to minimize a costfunction, and wherein the cost function can be used to measure how closethe reference d and q-axis stator currents from the flux-weakening andMTPA neural network match the ideal optimal reference d and q-axisstator currents for different reference torque and motor parameters. 2.The method of claim 1, wherein the neural network vector control systemincludes controller neural network, wherein the controller neuralnetwork is a feedforward network comprising an input layer, multiplehidden layers and an output layer, wherein the controller neuralnetwork: receives a plurality of inputs at the input layer, wherein theplurality of inputs include at least reference d and q-axis statorcurrents from the flux-weakening and MTPA neural network and d andq-axis actual stator currents and other signals that can be generatedfrom the reference and actual d and q-axis stator currents, wherein theother signals comprise error signals between the reference and actual dand q-axis stator currents and integrals of the error signals; outputs acompensating dq-control voltage at the output layer, wherein thecontroller neural network is configured to optimize the compensatingdq-control voltage based on the plurality of inputs; and controls theSVPWM converter using the compensating dq-control voltage, wherein thecontroller neural network is trained as a recurrent network to minimizea cost function of the ADP algorithm using a forward accumulationthrough time (“FATT”) algorithm, wherein the cost function can be usedto measure how close the actual motor d and q-axis stator currents matchthe reference d and q-axis stator currents, wherein the controllerneural network is trained offline at times when the IPM AC electricalmachine is not operating.
 3. The method of claim 1, wherein theparameter estimator neural network is a feedforward network comprisingan input layer, multiple hidden layers and an output layer, wherein saidparameter estimator neural network: receives a plurality of inputs atthe input layer, wherein the plurality of inputs include at least d andq-axis actual stator currents and estimated d and q-axis stator currentsfrom an adjustable IPM motor model and other signals that can begenerated from the actual and estimated d and q-axis stator currents,wherein the other signals comprise error signals between the estimatedand actual d and q-axis stator currents and integrals of the errorsignals; and outputs estimated motor parameters L_(d), L_(q), ω_(e), andψ_(pm) at the output layer, wherein the parameter estimator neuralnetwork together with the adjustable IPM motor model is configured tooptimize the IPM motor parameter estimation based on the plurality ofinputs; wherein the parameter estimator neural network is trained as arecurrent network to minimize a cost function of the ADP algorithm usinga forward accumulation through time (“FATT”) algorithm, wherein the costfunction can be used to measure how close the estimated d and q-axisstator currents from the adjustable IPM motor model match the actual dand q-axis stator currents, wherein the adjustable IPM motor modelreceives estimated motor parameters L_(d), L_(q), ω_(e), and ψ_(pm) fromthe parameter estimator neural network as well as the actual d andq-axis stator voltages of the IPM motor, and outputs the estimated d andq-axis stator currents, wherein the parameter estimator neural networkis trained offline at times when the IPM AC electrical machine is notoperating, wherein from the estimated motor parameters, an electricalrotational speed of the IPM AC electrical machine and a rotor positionof the IPM AC electrical machine can be obtained, wherein the estimatedelectrical rotational speed and the estimated rotor position can enablesensorless control for the IPM AC electrical machine.
 4. The method ofclaim 1, wherein the cost function is one of a Levenberg-Marquardt or abackpropagation training algorithm.
 5. The method of claim 1, whereinthe flux-weakening and MTPA neural network is trained offline at timeswhen the IPM AC electrical machine is not operating.
 6. A system forcontrolling an interior-mounted permanent magnet (IPM)alternating-current (AC) electrical machine comprising: a space vectorpulse-width modulated (SVPWM) converter operably connected between anelectrical power source and the IPM AC electrical machine; a neuralnetwork vector control system operably connected to the SVPWM converter,the neural network vector control system comprising a processor and acontroller neural network configured to implement an approximate dynamicprogramming (ADP) algorithm for training the neural network vectorcontrol system when the IPM AC electrical machine is not operating; aparameter estimator neural network operably connected to an output ofthe SVPWM converter through a dq-axis converter, wherein the parameterestimator neural network comprises a processor and wherein the parameterestimator neural network is configured to receive as inputs d and q-axiscurrents and d and q-axis voltages representative of current and voltageoutput from the SVPWM converter, capture and estimate parameters for theIPM AC electrical machine including at least an electrical rotationalspeed of the IPM AC electrical machine and a rotor position of the IPMAC electrical machine, and implement an ADP training algorithm, whereinan adjustable IPM motor model is applied with the parameter estimatorneural network to provide the estimated d and q-axis currents, whereinthe estimated d and q-axis currents are compared with actual d andq-axis currents and fed-backed as inputs to the estimator neuralnetwork, wherein the parameter estimator neural network system istrained at times when the IPM AC electrical machine is not operating; aflux weakening and maximum torque per ampere (MTPA) neural networkoperably connected to the neural network vector control system and theparameter estimator neural network, wherein the flux-weakening and MTPAneural network comprises at least a processor, an input layer, multiplehidden layers and an output layer, and is configured to receive as aninput a desired torque command and receive as inputs from the parameterestimator neural network at the input layer a reference torque, L_(d),L_(q), ω_(e), and ψ_(pm), wherein L_(d) and L_(q) are stator d- andq-axis inductances, ω_(e) is the IPM AC electrical machine's electricalrotational speed, and ψ_(pm) is a flux linkage of a permanent rotormagnet of the IPM AC electrical machine, and wherein the rotor positionof the IPM AC electrical machine is estimated from ω_(e) using theparameter estimator neural network and the rotor position is used toconvert the current and voltage outputs from the SVPWM converter totheir d and q-axis currents and d and q-axis voltages representative ofcurrent and voltage output from the SVPWM converter, wherein theflux-weakening and MTPA neural network is configured to output the d andq-axis currents to the controller neural network at the output layer asreference d and q-axis stator currents, wherein the flux-weakening andMTPA neural network is trained to minimize a cost function, and whereinthe cost function can be used to measure how close the reference d andq-axis stator currents from the flux-weakening and MTPA neural networkmatch the ideal optimal reference d and q-axis stator currents fordifferent reference torque and motor parameters.
 7. The system of claim6, wherein the controller neural network is a feedforward networkcomprising an input layer, multiple hidden layers and an output layer,wherein the controller neural network: receives a plurality of inputs atthe input layer, wherein the plurality of inputs include at leastreference d and q-axis stator currents from the flux-weakening and MTPAneural network, d and q-axis actual stator currents, and other signalsthat can be generated from the reference d and q-axis stator currentsand actual d and q-axis stator currents, wherein the other signalsinclude error signals between the reference and actual stator currentsand integrals of the error signals; outputs a compensating dq-controlvoltage at the output layer, wherein the controller neural network isconfigured to optimize the compensating dq-control voltage based on theplurality of inputs; and controls the SVPWM converter using thecompensating dq-control voltage, wherein the controller neural networkis trained as a recurrent network to minimize a cost function of the ADPalgorithm using a forward accumulation through time (“FATT”) algorithm,wherein the cost function can be used to measure how close the actual dand q-axis stator currents match the reference d and q-axis statorcurrents, wherein the controller neural network is trained offline attimes when the IPM AC electrical machine is not operating.
 8. The systemof claim 6, wherein the parameter estimator neural network is afeedforward network comprising an input layer, multiple hidden layersand an output layer, wherein said parameter estimator neural network:receives a plurality of inputs at the input layer, wherein the pluralityof inputs include at least d and q-axis actual stator currents andestimated d and q-axis stator currents from an adjustable IPM motormodel and other signals that can be generated from the actual andestimated d and q-axis stator currents, wherein the other signalsinclude error signals between the estimated and actual stator currentsand integrals of the error signals; and outputs estimated motorparameters L_(d), L_(q), ω_(e), and ψ_(pm) at the output layer, whereinthe parameter estimator neural network together with adjustable IPMmotor model is configured to optimize the IPM motor parameter estimationbased on the plurality of inputs; wherein the parameter estimator neuralnetwork is trained as a recurrent network to minimize a cost function ofthe ADP algorithm using a forward accumulation through time (“FATT”)algorithm, wherein the cost function can be used to measure how closethe estimated d and q-axis stator currents from the adjustable IPM motormodel match the actual d and q-axis stator currents, wherein theadjustable IPM motor model receives estimated motor parameters L_(d),L_(q), ω_(e), and ψ_(pm) from the parameter estimator neural network aswell as the actual d and q-axis stator voltages of the IPM motor, andoutputs the estimated d and q-axis stator currents, wherein theparameter estimator neural network is trained offline at times when theIPM AC electrical machine is not operating, wherein from the estimatedmotor parameters, an electrical rotational speed of the IPM ACelectrical machine and a rotor position of the IPM AC electrical machinecan be obtained, wherein the estimated electrical rotational speed andthe estimated rotor position can enable sensorless control for an IPM ACelectrical machine.
 9. The system of claim 6, wherein the cost functionis one of a Levenberg-Marquardt or a backpropagation training algorithm.10. The system of claim 6, wherein the flux-weakening and MTPA neuralnetwork is trained offline at times when the IPM AC electrical machineis not operating.
 11. A system for controlling an interior-mountedpermanent magnet (IPM) alternating-current (AC) electrical machinecomprising: a space vector pulse-width modulated (SVPWM) converteroperably connected between an electrical power source and the IPM ACelectrical machine; a neural network vector control system operablyconnected to the SVPWM converter, the neural network vector controlsystem comprising a processor and a controller neural network configuredto implement an algorithm for training the neural network vector controlsystem when the IPM AC electrical machine is not operating; a parameterestimator neural network operably connected to an output of the SVPWMconverter through a converter, wherein the parameter estimator neuralnetwork comprises a processor and wherein the parameter estimator neuralnetwork is configured to receive as inputs d and q-axis currents and dand q-axis voltages representative of current and voltage output fromthe SVPWM converter, capture and estimate parameters for the IPM ACelectrical machine including at least an electrical rotational speed ofthe IPM AC electrical machine and a rotor position of the IPM ACelectrical machine, and implement an ADP training algorithm; a fluxweakening and maximum torque per ampere (MTPA) neural network operablyconnected to the neural network vector control system and the parameterestimator neural network, wherein the flux-weakening and MTPA neuralnetwork comprises at least a processor, an input layer, multiple hiddenlayers and an output layer, and is configured to receive as an input adesired torque command and receive as inputs from the parameterestimator neural network at the input layer a reference torque, L_(d),L_(q), ω_(e), and ψ_(pm) wherein L_(d) and L_(q) are stator d- andq-axis inductances, ω_(e) is the IPM AC electrical machine's electricalrotational speed, and ψ_(pm) is a flux linkage of a permanent rotormagnet of the IPM AC electrical machine, and wherein the rotor positionof the IPM AC electrical machine is estimated from ω_(e) using theparameter estimator neural network, wherein the flux-weakening and MTPAneural network is configured to output the d and q-axis currents to thecontroller neural network at the output layer as reference d and q-axisstator currents, wherein the flux-weakening and MTPA neural network istrained to minimize a cost function, and wherein the cost function canbe used to measure how close the reference d and q-axis stator currentsfrom the flux-weakening and MTPA neural network match the ideal optimalreference d and q-axis stator currents for different reference torqueand motor parameters.
 12. The system of claim 11, wherein the algorithmcomprises an approximate dynamic programming (ADP) algorithm.
 13. Thesystem of claim 11, wherein the converter is a dq-axis converter. 14.The system of claim 11, wherein the parameter estimator neural networksystem is trained at times when the IPM AC electrical machine is notoperating.
 15. The system of claim 11, wherein, the estimated d andq-axis currents are compared with actual d and q-axis currents andfed-back as inputs to the estimator neural network.
 16. The system ofclaim 11, wherein an adjustable IPM motor model is applied with theparameter estimator neural network to provide the estimated d and q-axiscurrents.
 17. The system of claim 11, wherein the rotor position is usedto convert the current and voltage outputs from the SVPWM converter to dand q-axis currents and d and q-axis voltages representative of currentand voltage output from the SVPWM converter.
 18. The system of claim 11,wherein the parameter estimator neural network system is trained using acloud-computing environment.